Lead Scoring

Prioritize prospects by predicted value, not just firmographics.

Beyond Firmographic Scoring

Traditional lead scoring: “Manufacturing company, 50-200 employees, São Paulo = 75 points”

The problem: Two companies matching that profile can have completely different outcomes. One becomes your best customer, the other churns in 3 months. Firmographics don’t predict success.

You define the target outcome (conversion, high-LTV, retained after 12 months), we train a domain-specific model on top of our foundation. The result is a probability score between 0 and 1 calibrated to your definition of success.

You Provide	Avra Delivers
Historical outcomes (converted/not, high-LTV/not)	Trained model specific to your target
Your definition of “good”	Probability score (0-1) for each entity
Labeled seed data	Calibrated predictions across your universe

This same pattern applies across use cases—credit risk, churn, fraud. Define the target, we train the downstream model.

Scoring Your Pipeline

Your CRM Lead	Avra Score Output
`company: Acme Corp`	`conversion_probability: 0.87`
`cnpj: 12.345.678/0001-99`	`ltv_decile: 9`
`source: webinar`	`ltv_range: "R$ 80k-120k"`

Operationalizing Scores

Routing

Probability > 0.8: Route to senior AE, fast-track onboarding
Probability 0.5-0.8: Standard sales process
Probability < 0.5: Nurture sequence, don’t invest direct sales time

Prioritization

Sort daily lead list by score
SDRs work top-down, not first-in-first-out
Result: Same team, more revenue

Qualification

Replace subjective “gut feel” qualification
Score provides objective baseline
Sales adds context, doesn’t start from zero

Powered by two foundations

Lead scoring composes both Avra foundations. The Graph Foundation Model brings entity trajectories and network position from the broader economy — which entities are growing, which are showing stress, who counterparties cluster around. Your Relational Foundation Model brings the patterns specific to your business — what your converters look like, which signals predict your high-LTV customers. The downstream model is trained on both, and feeds signal back into your RFM with every retrain.

Customer Data Needed

Data	Purpose
Historical outcomes	Converted/not, high-LTV/not, retained/churned — your definition of “good”
CRM records	Lead source, engagement history, firmographic attributes
Transaction history	Revenue patterns, product adoption, usage signals

Output Schema

Field	Description
`conversion_probability`	Probability (0-1) that the entity converts based on your definition
`ltv_decile`	Predicted lifetime value bucket (1-10)
`risk_factors`	Key signals driving the prediction (network growth, sector health, counterparty quality)

Evaluation Metrics

ROC-AUC — Primary discrimination: how well the model separates converters from non-converters.
Lift at top deciles — Operational metric: how much better the model’s top-ranked leads perform vs. random selection.
Calibration — Predicted probabilities should match observed conversion rates across score bands.

Combining with Your Data

The best results come from combining Avra scores with your first-party signals:

Input	Source
`avra_lead_score`	Avra API
`product_engagement`	Your product analytics
`recency`	Your CRM
`source_quality`	Your marketing data

Use our embeddings as features in your model, or use our score directly. Either way, you’re adding context your CRM can’t see.